Reweighted Low-Rank Matrix Recovery and its Application in Image Restoration | IEEE Journals & Magazine | IEEE Xplore

Reweighted Low-Rank Matrix Recovery and its Application in Image Restoration


Abstract:

In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate its application for robust image restoration. In the literature, principal componen...Show More

Abstract:

In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate its application for robust image restoration. In the literature, principal component pursuit solves low-rank matrix recovery problem via a convex program of mixed nuclear norm and ℓ1 norm. Inspired by reweighted ℓ1 minimization for sparsity enhancement, we propose reweighting singular values to enhance low rank of a matrix. An efficient iterative reweighting scheme is proposed for enhancing low rank and sparsity simultaneously and the performance of low-rank matrix recovery is prompted greatly. We demonstrate the utility of the proposed method both on numerical simulations and real images/videos restoration, including single image restoration, hyperspectral image restoration, and background modeling from corrupted observations. All of these experiments give empirical evidence on significant improvements of the proposed algorithm over previous work on low-rank matrix recovery.
Published in: IEEE Transactions on Cybernetics ( Volume: 44, Issue: 12, December 2014)
Page(s): 2418 - 2430
Date of Publication: 19 March 2014

ISSN Information:

PubMed ID: 25415947

Funding Agency:


References

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